Organ and lesion segmentation.
The medical segmentation was significantly advanced via deep encoder-decoder structures (e.g., U-Net [1]}). This architecture contains a contracting path (i.e., encoder) to capture global context and a symmetric expanding path (i.e., decoder) to obtain precise localization. There were several works built upon U-Net. In [2]}, skip connections were utilized in the first and last convolutional layers to segment lesions in brain. A V-Net was proposed in [3]} to segment brain's anatomical structures. It followed 3D U-Net structure consisting of 3D convolutional layers. A dual pathway and multi-scale 3D-CNN architecture was proposed in [4]} to generate both global and local lesion representations in the CNN for brain lesion segmentation. Existing medical methods mainly utilized deep encoder-decoder architectures for end-to-end segmentation of organs and lesions.
